联合主题模型的标签聚类方法

摘要

Improving the clustering quality of social tags is a key problem in the semantics recognition of tags.A joint topic model based on resource is proposed to cluster tags.Firstly,reference relations of the resource are utilized to acquire the authority scores of resource by using random walk method.Secondly,the resource authority is applied to set the weights of two binary relations of resource-tag and resource word.Grounded on that,the joint latent Dirichlet allocation (LDA) model of the word and the tag based on resource weighted is constructed.By iterative learning,the latent topics of the tag are acquired,and the clusters are decided according to the maximum membership degree of the tag.The results show that the proposed method has a better clustering performance than other tag clustering methods based on resource.%提升标签聚类的质量是识别标签语义的一个关键问题.文中提出基于资源的联合主题模型标签聚类方法.利用资源的引用关系,采用随机游走的方法获取资源的权威度分数,以此设置“资源-标签”和“资源-词”这2个二元关系的权重.在此基础上,构建基于资源加权的词与标签的联合潜在狄利克雷分布(LDA)模型,通过迭代学习,获取标签的潜在主题,并根据主题最大隶属度聚类标签.实验表明,相比其它基于资源的标签聚类方法,文中方法能获取更好的聚类效果.